Computer Simulations May Help CEOs to Divine the Future

February 8, 2006

Making good business and production decisions isn’t as straightforward or intuitive as it used to be because today’s global market and business environment is exceedingly more complex.

“When Henry Ford started making Model Ts, the goal was mass production. “He wanted to stamp out thousands of the same thing and didn’t have to change the basic setup once it worked,” says Young-Jun Son, an assistant professor in Systems and Industrial Engineering (SIE) at The University of Arizona.

“In that climate, business and production decisions on things like how to make mass production work were relatively intuitive,” he added.

Today’s systems and decisions are nowhere near as transparent. Customers want many different, customized products. The production line and supply chain are fluid and suppliers, manufacturers, distributors and customers are spread across the globe.

That’s why Son is a big fan of computer simulation. He sees it as a kind of electronic crystal ball that can analyze thousands of alternative futures in much the same way that IBM’s Big Blue analyzed 200 million moves per second to defeat Chess champion Garry Kasparov in 1997.

“We can simulate decisions, sometimes running through millions of scenarios, to see what will happen in great detail,” Son says.

FROM SHOP FLOOR TO TOP FLOOR

He does this by combining information technology with computer simulations that model all kinds of manufacturing and distribution systems. The many applications of what he calls “Modeling from Shop Floor to Top Floor,” include accurately predicting the success of a corporate merger or partnership.

By modeling the merged manufacturing and distribution systems of two or more companies, executives could use machine intelligence to divine the future, to see if a merger or partnership would pay off and how well.

High-speed digital communication makes this possible. “Today, we can get a lot of data in real time that traditionally was not available,” Son explained. “This includes real-time data on inventory, orders and materials from suppliers, for instance. So we can evaluate alternate futures with models that are linked to the most recent data.”

The decisions Son and his students model can influence everything from long-term, boardroom choices to everyday, factory-floor scheduling.

They can answer questions such as: In what sequence do I need to fill these orders? Which products should I build first? What is the optimum way to maintain equipment to maximize production and profits? When should we buy materials? How often should we send out orders for raw materials and how much should we buy at a time? How can we prepare robust production plans that will respond to future machine breakdowns and market uncertainties?

Son uses several computers working in parallel to simulate the real world in cyberspace. “The computers are exchanging messages and coordinating in time to share information and transfer physical goods,” he said. Each computer can represent a separate factory. This could be a supplier in Texas, for instance, and a manufacturing plant in Tucson.

Despite the promise of simulations, however, many companies still use less sophisticated analytical techniques to arrive at important decisions, he noted. “For example, a lot of companies still do their capacity analysis using spreadsheets and complicated mathematical equations,” Son said. “If they are considering a big contract, for instance, they will want to know if their production facilities will allow them to deliver the order on time.”

But the answers produced by corporate spreadsheets often are 60 to 70 percent different from those Son gets from simulation, which indicates that the company’s best guess may be flawed more than 50 percent of the time.

SOMETIMES CHAOS THEORY APPLIES

In some cases, there are so many factors involved that uncertainties can push the simulation into an astable state in which chaos theory applies. In chaos theory, infinitesimally small differences in initial conditions can lead to wildly varying results.

A small factor, such as a tiny difference in the cost of a minor component, might not appear important, but in a chaotic system it could mean the difference between profit and loss.

“Chaotic behavior is often considered bad, but sometimes it can affect the profit in a good sense,” Son said. “If we understand the system, we sometimes can use it to our advantage.”

Some companies are using simulation software for internal planning but none are yet running a complete simulation package that’s fully linked to suppliers and other companies, Son noted.

“Simulation is getting bigger and bigger in industry but the supply chain simulation that I am talking about is still not fully developed,” he said.

“Companies aren’t using this kind of simulation yet because they are afraid it may expose some of their proprietary information. Our challenge now is to produce models that share information but at the same time hide proprietary data on things like production capacity and processes.”

TIME CAN BE A PROBLEM

Another problem is time. Although the simulation models have proven to be much more accurate than the most sophisticated spreadsheets, the simulations can take a long time to run.

“If we talk about large issues, such as capacity analysis, it’s OK that it may take three or four days to run the simulation because that is a big decision that will affect operations for six months or a year,” Son said.

“It’s OK to spend a week to make a big decision. But we also want to use simulations to manage daily operations. If we need to come up with a production schedule for today or for this week at a factory, we can’t spend all morning deciding what to do.”

The challenge now is to speed up the process, to decide which answers can be “good enough” and which ones need to be precise.

“Increasing the speed without compromising accuracy is where a lot of our research is directed now,” Son said.

But the real test will come in industry and Son is anxious to go there.

“In order to validate this technology, it needs to be tested in industry,” he said. “So my goal is to create simulations that are accurate and fast and that run in a secure environment that protects proprietary information. I think this will help convince industry to adopt this kind of distributed, decision-making simulation.”